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A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost · 2d agoAI research1

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

Hacker News · AI · 8d agoAI tools & infraHN 141↑ · 52 comments1

Multiple ArtraDownloader Variants Used by BITTER to Target Pakistan

BITTER used three ArtraDownloader variants since 2015 to target Pakistan, China, and Saudi Arabia, deploying BitterRAT and exploiting CVE-2017-11882 in one wave.

Palo Alto Unit 42 documents ArtraDownloader, a previously unreported downloader family used by the suspected South Asian group BITTER, with three variants and roughly 80 unique samples dating back to February 2015. Between September 2018 and January 2019, BITTER used spearphishing documents hosted on compromised Pakistani websites to target Pakistan and, for the first time, Saudi Arabia; one RTF sample exploited the EQNEDT vulnerability CVE-2017-11882. The downloader retrieves BitterRAT over HTTP, uses simple registry keys for persistence, and byte-level string obfuscation. Infrastructure overlap with the previously reported MY24 InPage-exploit payload was also observed.

Palo Alto Unit 42 · 29d agoThreat actor in the wildCVE-2017-11882

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space · 3d agoModel release1

Hardware Fingerprinting FTQC via Quantum Decoder Timing

Quantum decoder timing on IBM Heron processors forms a side channel enabling device fingerprinting with 89% accuracy and workload inference.

The work demonstrates that wall-clock syndrome-decoding times on fault-tolerant quantum computers constitute a novel hardware side channel. Using per-shot decoder timings from three IBM Heron processors collected over 68 days, a passive observer can reconstruct detector-firing distributions, estimate logical error rate, infer code distance, and fingerprint the specific device with up to 89% accuracy versus 33% for random guessing. Noisy simulation based on Google's 105-qubit Willow processor distinguishes nine surface-code patches at 81% accuracy, showing the channel persists across vendors and code families.

arXiv cs.CR · 5d agoResearch

PostgreSQL Fixes 12-Year-Old Logical Decoding Flaw Enabling Replication-Role Code Execution

PostgreSQL fixed CVE-2026-6471 (CVSS 7.2), a 12-year-old logical decoding flaw letting replication-role accounts execute arbitrary code as the database OS user.

PostgreSQL patched CVE-2026-6471 (CVSS 7.2), a flaw present since logical decoding shipped in 9.4 (2014) that lets an account with the REPLICATION attribute run arbitrary code as the database server's OS user when wal_level is logical. Versions before 18.6, 17.11, 16.15, 15.19, and 14.24 are affected; exploitation requires such an account, which backup, CDC, and monitoring tooling routinely holds. The fix adds an output_plugin_libraries whitelist, so administrators using non-default plugins such as wal2json and decoderbufs must add them and reload configuration after updating. Cyera Research, whose write-up names the flaw PostGREShell, demonstrated escalating a replication user to superuser with persistence; no PoC or KEV entry existed as of September 4.

The Hacker News · 11d agoVulnerabilityCVE-2026-64711

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models · 11d agoModel release1

DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse

DeepSeek released open-weight V4.1-Flash, a 552B MoE model with 1M context and FP4 KV cache, beating Opus-5 and GPT-5.6 Sol on agent benchmarks.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone plus 196B Engram parameters, activating 8B parameters at prefill and 16B at decode, with a 1M-token context window. It introduces a causal encoder-decoder design, Compressed Sparse Attention 2, and FP4 (E2M1) KV cache quantization, cutting global KV cache to 890 bytes per token, about 1/4 of V4-Flash and 437x smaller than V1. Pre-training covered 45T multimodal tokens; the MIT-licensed weights ship on Hugging Face with vLLM and SGLang support. It scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, ahead of Opus-5 and GPT-5.6 Sol.

MarkTechPost · 5d agoModel release1

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.

TransNormal-2 is a FLUX.2-based rectified-flow framework for monocular surface-normal estimation with single-step deterministic inference. The authors quantify that VAE 8x spatial compression introduces 1.3-8.5 degrees of mean angular error even on ground-truth normals, with edge error up to 2.8x the global error. The method adds geometry-aware pixel-space losses and an RGB-guided Geometric Refinement Module to correct boundary-localized decoding errors. It matches or exceeds MoGe-2 on all eight reported metrics using only 1.4% as many task-specific annotations, and cuts transparent-object MAE by 4.2 degrees on ClearGrasp and 3.1 degrees on ClearPose.

Hugging Face daily papers · 10d agoAI research

ClearFake WebDAV infection chain delivers Amatera stealer, ZigCryptoStealer, and NetSupport Manager

Cisco Talos details ClearFake WebDAV chains delivering Amatera stealer to a Ukrainian government organization, with cryptocurrency and credential theft payloads.

Cisco Talos investigated DLL executions named 'verification.google' via WebDAV UNC paths at a Ukrainian government organization, tracking the actor as UAT-10820 and assessing with moderate confidence the activity is Russian and opportunistic rather than targeted. The infection chain uses ClearFake JavaScript injected via a Cloudflare Worker, EtherHiding storage on BNB Smart Chain contracts, and a ClickFix fake Google CAPTCHA prompt to deliver Amatera stealer. Secondary payloads differ by C2: one loader deploys ZigCryptoStealer with a Go reverse TCP proxy and a vulnerable driver that kills EDR, while the other installs an unauthorized NetSupport Manager with a Russia-based C2. Similar Amatera chains were separately documented by Malwarebytes and Blackpoint Cyber, but with no shared infrastructure.

Cisco Talos · 7d agoMalware in the wild1

RANCOR: Targeted Attacks in South East Asia Using PLAINTEE and DDKONG Malware Families

Unit 42 names RANCOR, a previously unidentified espionage group targeting Southeast Asian political entities with newly discovered PLAINTEE and DDKONG malware families.

Unit 42 tracks a previously unidentified group it dubs RANCOR conducting highly targeted espionage attacks in Singapore and Cambodia using two custom malware families, DDKONG and PLAINTEE. Attacks appear to have begun with spear phishing, with decoy political news documents hosted on legitimate sites including a Cambodian government website and Facebook. Infrastructure links to IP 89.46.222[.]97 and KHRAT-associated domains such as facebook-apps[.]com, and the activity is grouped into two clusters tied together by PLAINTEE usage and similar targeting.

Palo Alto Unit 42 · 29d agoThreat actor1

OpenAI, Anthropic, Google API Flaw Let Weaker AI Models Decode Stronger Models' Reasoning

Researchers show encrypted reasoning blocks in OpenAI, Anthropic, and Google APIs can be replayed to recover hidden reasoning and secrets like API keys.

Researchers demonstrated that encrypted reasoning objects from OpenAI, Anthropic, and Google reasoning APIs could be replayed across sessions, users, and models, letting weaker same-family models act as decoders of hidden reasoning. Across 6,708 public agent trajectories they decoded 315,320 thinking blocks and found 704 privacy artifacts from real user sessions, including 62 API keys, 33 passwords, 24 access tokens, and seven private keys. The replayable blocks also enabled invisible prompt-injection proof-of-concepts; the main extraction attack is no longer reproducible as of August 2026 following mitigations, though no vendor has publicly acknowledged the flaw.

The Hacker News · Aug 12, 2026AI safety & security1

Tracking OceanLotus’ new Downloader, KerrDown

Unit 42 identifies KerrDown, a new OceanLotus (APT32) downloader active since 2018 targeting Vietnamese speakers via malicious macros and DLL side-loading.

Unit 42 tracks KerrDown, a previously undocumented downloader family used by OceanLotus (APT32) since at least early 2018, primarily targeting Vietnam or Vietnamese-speaking individuals. Delivery uses macro-laced Microsoft Office documents embedding base64-encoded 32-bit and 64-bit DLLs, and RAR archives containing a legitimate program abused for DLL side-loading. KerrDown is dropped as main_background.png, downloads a DES-encrypted payload from a URL, and executes it directly in memory. Researchers used Jaccard-index similarity analysis to identify the new family, connect campaign samples, and infer patterns in the group's working hours and days.

Palo Alto Unit 42 · 29d agoMalware in the wild1

The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.

The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.

Hugging Face daily papers · 7d agoAI research2